PROJECT TITLE :

A Fast Converging Channel Estimation Algorithm for Wireless Sensor Networks - 2018

ABSTRACT:

A group-membership affine projection algorithm is proposed which will estimate a complicated-valued channel matrix using a set of advanced-valued pilots within the presence of additive white Gaussian noise. It is shown that the algorithm converges faster than the well-known set-membership normalized least mean sq. algorithm (SM-NLMS) whereas it resolves the high steady-state error value and the complexity issues in the regular affine projection algorithm. The quick convergence of the proposed algorithm suggests that that a shorter training sequence within every information block is required, that in turn improves the effective bit rate. This quick convergence is a lot of pronounced when the pilot vectors are highly correlated. We tend to incorporated the set-membership filtering framework into our study to scale back the computational complexity of the algorithm and preserve energy in the WSNs. Other studies have shown the prevalence of the adaptive filtering algorithms, and in explicit the NLMS algorithm, over different alternatives in varied signal processing areas in WSNs, so, our proposed algorithm is a powerful substitute for a selection of algorithms. In other words, the implementation of various signal processing algorithms for various purposes can get replaced with the implementation of the proposed multipurpose algorithm. In this Project, we tend to mix the results of our previous studies and prove the convergence of the algorithm. Furthermore, the steady-state analysis in the output mean square error is presented for 2 cases of pilot signals, and within the conducted simulations, the MSE performance of the algorithm is compared with the regular affine projection algorithm and the SM-NLMS algorithm.


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